Clustering Random Walk Time Series

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9389)


We present in this paper a novel non-parametric approach useful for clustering independent identically distributed stochastic processes. We introduce a pre-processing step consisting in mapping multivariate independent and identically distributed samples from random variables to a generic non-parametric representation which factorizes dependency and marginal distribution apart without losing any information. An associated metric is defined where the balance between random variables dependency and distribution information is controlled by a single parameter. This mixing parameter can be learned or played with by a practitioner, such use is illustrated on the case of clustering financial time series. Experiments, implementation and results obtained on public financial time series are online on a web portal


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Hellebore Capital ManagementParisFrance
  2. 2.Ecole PolytechniquePalaiseauFrance

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